Changelog

BU-30B-A3B-Preview is here.

• 30B total parameters with only 3B active at inference time
• 200 tasks per $1 — 4x more cost-efficient than BU 1.0

Try it out on the library or on our cloud:

from browser_use import Agent
from browser_use.llm import ChatBrowserUse
 
# Initialize with the new model
llm = ChatBrowserUse(model="bu-30b-a3b-preview")
 
agent = Agent(
    task="Your task here",
    llm=llm
)
 
result = await agent.run()

Download from Hugging Face.

Turn plain-text descriptions into production-ready API endpoints.

1. Create – describe your automation goal
2. Analyse – auto-generated parameter & output schemas
3. Run – execute via API with typed inputs

Create via Dashboard or API. Full docs.

Cloud MCP Server

A hosted Model Context Protocol (MCP) server that enables AI assistants to control browser automation. It works with any HTTP-based MCP client, including Claude Code.

MCP Server URL: https://api.browser-use.com/mcp

This is an HTTP-based MCP server designed for cloud integrations and remote access. If you need a local stdio-based MCP server for Claude Desktop, use the free open-source version: uvx browser-use --mcp

Implementation Details

Check out the documentation.

Gemini 3 support

It’s the best model for Browser Use.

Teams

The option to add multiple team members to your cloud project (same billing, same credits throughout organization). Available on cloud.browser-use.com.

Library judge

When task is completed, you can get llm-as-a-judge evaluation of the task - the easiest way to evaluate your own workflows (available on the library and cloud). If you need more advanced evals contact us.

const task1 = await client.tasks.createTask({
  task: "Whats the weather in SF.",
  schema: TaskOutput,
  llm: "browser-use-llm",
  judge: true
});
 
const result1 = await task1.complete();
result1.judgeVerdict, result1.judgement

Browse community workflows at /templates. Clone and run instantly. Contribute your workflows here. We share accepted contributions on X/LinkedIn.

Browser Use is now SOC 2 Type II compliant. Access the trust report at /security/soc2.

Sync your local Chrome profile to the cloud. Your agents can now use authenticated sessions, cookies, and preferences from your local browser.

export BROWSER_USE_API_KEY=bu_ab1...
curl -fsSL https://browser-use.com/profile.sh | sh

CodeAgent writes Python and JavaScript for native browser interaction via CDP. Custom LLM trained for code generation only.

from browser_use import CodeAgent, ChatBrowserUse
 
agent = CodeAgent(
    task=task,
    llm=ChatBrowserUse(), # requires our LLM
)
await agent.run()

Use cases:

• Extract 1000 products, filter by price
• Get 2000 GitHub stargazers with metadata
• Scrape projects, filter by stars, export CSV

Available in 0.9.0 (OSS). Grab the API key from cloud.

We built a special LLM that reduces the latency by 6x while keeping the same performance. The agents can now take 20 steps per minute.

from browser_use import Agent, ChatBrowserUse
 
# Initialize the model
llm = ChatBrowserUse()
 
# Create agent with the model
agent = Agent(
    task="...", # Your task here
    llm=llm
)

For more technical deep dive read the blog at /posts/llm-gateway.

Features

• Persistent profiles - Maintain cookies, local storage, and session data across browser instances
• Proxy support - Route traffic through proxies to bypass geo-restrictions and bot detection
• File handling - Download files directly through the API without manual intervention

Browser Sessions with SDK

Access raw browser instances via Chrome DevTools Protocol:

from browser_use_sdk import BrowserUse
 
client = BrowserUse(api_key="bu_...")
 
# Basic browser session
browser_session = client.browsers.create_browser_session(
    timeout=30  # minutes
)
 
# With profile (inherit login state)
profile_browser = client.browsers.create_browser_session(
    profile_id="profile_123",
    proxy_country_code="US",
    timeout=60
)
 
print(f"CDP URL: {browser_session.cdp_url}")
print(f"Watch live: {browser_session.live_url}")

For more technical details, read the blog at /posts/browser-infra.

Combine precise element interactions with AI-driven automation in a single workflow.

from browser_use import Browser, Agent
from browser_use.llm.openai import ChatOpenAI
 
async def main():
    llm = ChatOpenAI(api_key="your-api-key")
    browser = Browser()
    await browser.start()
 
    # 1. Actor: Precise navigation and element interactions
    page = await browser.new_page("https://github.com/login")
    email_input = await page.must_get_element_by_prompt("username field", llm=llm)
    await email_input.fill("your-username")
 
    # 2. Agent: AI-driven complex tasks
    agent = Agent(browser=browser, llm=llm)
    await agent.run("Complete login and navigate to my repositories")
 
    await browser.stop()

In your prompts you can now prompt the agent to write JavaScript code to interact with elements on the website.

This enables more precise control and complex interactions that go beyond standard browser automation—perfect for handling edge cases or custom behaviors.

from browser_use import Agent
 
agent = Agent(
    task="Write JavaScript to extract all product prices from the page",
    llm=llm
)
 
result = await agent.run()

The agent will automatically write and execute the necessary JavaScript to complete your task.

You can control the cloud API using the typesafe SDK.

from browser_use_sdk import BrowserUse
 
client = BrowserUse(api_key="bu_...")
 
task = client.tasks.create_task(
    task="Search for the top 10 Hacker News posts and return the title and url.",
    llm="browser-use-llm"
)
 
result = task.complete()
 
result.output

For more information, check out the documentation.

This means faster execution, lower overhead, and direct control over browser behavior.

Read the full technical breakdown at /posts/playwright-to-cdp.